Can Computers Diagnose if You’re Depressed From Your Social Media?

Wednesday 9nd August 2017

Researchers have reported that computers can determine if someone is suffering from depression from the nature and content of people’s posts on Instagram, a photo sharing social media web site, with what researcher purport is a greater accuracy than GPs.

This is part of an interesting development in medical and computer research known as “machine learning”, where a computer looks a huge amount of data and can use it to spot patterns that can be missed by human expertise. Similar stories have focused on Deepmind’s project to aid diagnosis of various conditions, including the early signs of eye diseases that could cause blindness, detecting cancerous tissues in the head and neck.

The study, published in the journal EPJ Data Science and undertaken by researchers from the University of Vermont and Harvard University focuses on 43,000 images from 166 people’s social media accounts. The people who took part also completed a survey looking if they had a history of depression. The researchers then compared the images using various measurable criteria, including colour hue, brightness, vividness and colour saturation, the use of Instagram’s filters, the number of human faces in each post, the comments and likes, and how frequent the posts were. The features were compared between the two groups and various detection programmes were run to see how well they could predict those who had depression based on 100 Instagram posts.

This was then compared to a previous independent meta-analysis which found that GPs could diagnose accurately 42% of people with depression without using any validated questionnaires or measurements. The computer program’s score? 70%. Certain features were noted in the depressed group, such as less vibrant, darker images, more photos, more photos with phases, more photos without filters and photos that generated more comments but less likes. The filter they were most likely to use was the black and white convertor “inkwell”.

This has naturally led to suggestions that a computer algorithm could be rolled out to catch the early warning signs of depression in a way more accurate than GPs using Instagram images. There’s a number of problems with this theory, and the biggest come from the 42% statistic referenced earlier. This comes from a meta-analysis that used no measurement tools, essentially just looking and asking questions, which is unlikely to be the diagnosis process for depression from any GP, and while this means the Instagram detector beats a GP using no tools whatsoever, it isn’t too clear if the tool would be better than what is being currently used, which is the criteria used in clinical trials for a reason.

There is also an issue with selection bias, as any study reliant on a social media platform will have. Not only did the participants have to use Instagram on a frequent basis, they also had to provide access to all their posts, which led to a number of people dropping out and a small pool of participants, which would serve to heighten any skewing of results.

As allowing access to personal social media accounts, and allowing that to be the first step of diagnosis has a number of major implications regarding privacy and the nature of information shared online, there needs to be a lot more research and consensuses reached on how effective machine learning systems actually are for diagnosing depression.